Spatial Assessment of Meteorological Drought in Luzon Using the Standardized Precipitation Evapotranspiration Index (SPEI)

Drought is a major natural hazard causing significant agricultural, economic, and environmental damage. This makes the identification of drought-susceptible areas essential for effective resource management and mitigation planning. This study assessed the meteorological drought hazard of Luzon, Philippines, using the Standardized Precipitation Evapotranspiration Index (SPEI), a climatological indicator requiring only monthly rainfall and temperature data. Unlike the precipitation-based Standardized Precipitation Index (SPI), SPEI also accounts for atmospheric evaporative demand, making it more sensitive to warming driven drought. Ground-station and provincial scale resolution SPEI hazard mapping, that couples the index with AHP-weighted hazard scoring and geostatistical interpolation, has remained limited for Luzon. Sixteen years (2001–2016) of monthly rainfall and mean temperature records from 27 DOST-PAGASA weather stations across Luzon and its adjacent island provinces were analyzed to compute SPEI values using the SPEI package for R. A Drought Hazard Score (DHS) was developed based on the frequency of moderate, severe, and extreme drought categories, weighted using the Analytic Hierarchy Process (AHP). Drought hazard maps were generated using Co-Kriging interpolation, incorporating secondary rainfall and temperature data from WorldClim to enhance spatial prediction accuracy. Results revealed that western Luzon, including the National Capital Region and the Ilocos Region, exhibited high to very high drought hazard, while most other areas demonstrated moderate hazard. The 6-month and 12-month SPEI analyses showed spatially similar patterns, with eastern and southern Luzon experiencing moderate to severe drought during the 2015–2016 El Niño event. The contrast between the single-event SPEI snapshot and the long-term DHS reflects two distinct quantities: event intensity versus climatological recurrence, and is not contradictory. Interpolation prediction accuracy varied by surface: SRMS was 0.844 for SPEI-6 (well-calibrated), 1.60 for SPEI-12 (slightly over-confident), and 0.60 for both DHS surfaces (conservative); all values fall within ranges regarded as acceptable for sparse station networks. These findings provide crucial information for prioritizing irrigation infrastructure, water-storage planning, and drought mitigation across Luzon.